Eyewitness Accounts and Political Claims: Transnational Responses to the 2009 Postelection Protests in Iran
Bibliographic record
Abstract
Through an examination of the use of media in the 2009 postelection protests in Iran, this article contextualizes the use of social media as a form of eyewitness account within the broader context of media distrust in Iran and Iranian expatriate satellite television. What emerges through this analysis is a set of concerns related to the eyewitness account offered through digital media as a potentially uncontrollable and indeterminate form. As I show through a media analysis of online content and debates, these concerns are evident both in the context of Iran and among Iranians in the diaspora. In the latter case, this article considers the protests as a transnational media event, focusing on the use of social media in the Iranian diaspora, and examines how Iranians around the world have used social media to connect to the events in Iran as a form of activism and in the performance of Iranian identities. Surveys and interviews conducted with Iranians in Los Angeles and Toronto reveal among Iranian immigrants a deep concern with the global image of Iran, an emphasis on publicity as a form of political action, and anxieties related to the representation of the “Iranian community” in Western media.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".